With increased system complexity, Condition-Based Maintenance (CBM) becomes a promising solution to system safety by detecting faults and scheduling maintenance procedures before faults become severe failures resulting in catastrophic events. For CBM of many mechanical systems, fault diagnosis and failure prognosis based on vibration signal analysis are essential techniques. Noise originating f...

*Corr. Author’s Address: VTT Technical Research Centre of Finland, FI-02044, Espoo, Finland, [email protected] 655 Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in TwoDimension Domain Do, V.T. – Chong, U.-P. Van Tuan Do1,* – Ui-Pil Chong2 1 VTT Technical Research Centre of Finland, Finland 2 University of Ulsan, Department of Computer E...

The information extraction capability of two widely used signal processing tools, Hilbert Transform (HT) and Wavelet Transform (WT), is investigated to develop a multi-class fault diagnosis scheme for induction motor using radial vibration signals. The vibration signals are associated with unique predominant frequency components and instantaneous amplitudes depending on the motor condition. Usi...

A real time on-line transformer vibration monitoring system based on the Labview is proposed and applied in the monitoring of abnormal vibration of transformer caused by DC bias in this paper. The monitoring of the transformer body vibration and the signal online analysis can be well performed by this system. The vibration signals of the transformer body are detected by three acceleration senso...

Premature combustion that affects outputs, thermal efficiencies and lifetimes of internal combustion engine is called “knock effect”. However knock signal detection based on acoustic sensor is a challenging task due to existing of noise in the same frequency spectrum. Experimental results revealed that vibration signals, generated from knock, has certain frequencies related to vibration resonan...

The periodical transient impulses caused by localized faults are sensitive and important characteristic information for rotating machinery fault diagnosis. However, it is very difficult to accurately extract transient impulses at the incipient fault stage because the fault impulse features are rather weak and always corrupted by heavy background noise. In this paper, a new transient impulse ext...

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected wit...

The vibration signal is one of the significant signals that reflects the fault. In allusion to the shortcomings of traditional signal analysis method in the high-frequency and nonstationary signal analysis, the wavelet theory and approximate entropy algorithm are introduced into the signal analysis in order to propose a new vibration signal analysis (WTAEAVSA) method in this paper. In the propo...

This paper presents an empirical study of feature extraction methods for the application of low-speed slew bearing condition monitoring. The aim of the study is to find the proper features that represent the degradation condition of slew bearing rotating at very low speed (≈1 r/min) with naturally defect. The literature study of existing research, related to feature extraction methods or algori...

Time series analysis has been applied to structural monitoring signals for system damage identification in a number of research literatures. Among various time series analysis tools, univariate autoregressive modeling (AR) is one of the most commonly used methods because of its innate computational efficiency. In this paper, three autoregressive damage features extracted directly from the ambie...